Crowdsourcing algorithms to predict epileptic seizures

Epilepsy is highly different among individuals. Results showed different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. Image: Pixabay

A study by University of Melbourne researchers reveals clinically relevant epileptic seizure prediction is possible in a wider range of patients than previously thought, thanks to the crowdsourcing of more than 10 000 algorithms worldwide.

The contest focused on seizure prediction using long-term electrical brain activity recordings from humans obtained in 2013 from the world-first clinical trial of the implantable NeuroVista Seizure Advisory System.

Researchers rigorously evaluated the top algorithms and these findings are detailed in research published today in Brain: A Journal of Neurology.

University of Melbourne Dr Levin Kuhlmann, from the Graeme Clarke Institute and St Vincent’s Hospital Melbourne, said the contest was a huge success, with more than 646 participants, 478 teams and more than 10 000 algorithms submitted from around the world.

“Epilepsy affects 65 million people worldwide,” Dr Kuhlmann said.

“We wanted to draw on the intelligence from the best international data scientists to achieve advances in epileptic seizure prediction performance for patients whose seizures were the hardest to predict.”

Contestants developed algorithms to distinguish between 10-minute inter-seizure verses pre-seizure data clips and the top algorithms were tested on the patients with the lowest seizure prediction performance based on previous studies.

“Our results highlight the benefit of crowdsourcing an army of algorithms that can be trained for each patient and the best algorithm chosen for prospective, real-time seizure prediction.

“It’s about bringing together the world’s best data scientists and pooling the greatest algorithms to advance epilepsy research. The hope is to make seizures less like earthquakes, which can strike without warning, and more like hurricanes, where you have enough advance warning to seek safety.”

The research was led by the Graeme Clark Institute of Biomedical Engineering, in collaboration with St. Vincent’s Hospital Melbourne, Swinburne University of Technology, Mayo Clinic, Perelman School of Medicine at the University of Pennsylvania and Seer Medical.